Search Results (855)

Assurance of the integrity of every weld joint is highly desirable, and defect detection methods that can be applied to welds at high temperatures immediately after welding are required. The laser ultrasonic (LU) method, which generates ultrasonic waves in the target via pulsed laser irradiation, is a well-known technique for non-contact defect detection during welding. Ultrasonic waves excited in ablation mode exhibit large amplitudes and predominantly surface-normal propagation, which has driven extensive research into their application for weld inspection. However, owing to the size and weight of conventional equipment, such systems have largely been limited to bench-top experimental setups. To address this, we developed an LU robotic system incorporating a compact, lightweight laser source and an improved signal-processing system. We conducted experiments to measure signals and to detect backside slits in flat plates and blowholes in lap-fillet welds. Additionally, a method to improve the sensitivity of laser interferometers was investigated and demonstrated on smut-covered areas near weld beads. Full article

Lensless microscopy is a well-established imaging approach that replaces traditional lenses with phase modulators, enabling compact, low-cost, and computationally driven analysis of biological samples. In this work, we show how ray tracing simulations can be used to optimize lensless imaging systems for automated classification, particularly for detecting red blood cell (RBC) disease. Rather than improving the machine learning classification algorithm, our focus is on refining optical parameters such as element spacing and modulator type to maximize classification performance. We modeled a lensless microscope in Zemax OpticStudio (ray tracing) and compared the results against Fourier optics simulations. Despite not explicitly modeling diffraction, ray tracing produced classification results largely consistent with wave optics simulations, confirming its effectiveness for parameter optimization in lensless imaging setups used for classification tasks. Furthermore, to show the flexibility of the ray tracing model, we introduced a microlens array (MLA) as the phase modulator and performed the classification task on the generated patterns. These results establish ray tracing as an efficient tool for the optical design of lensless microscopy systems intended for machine learning based biomedical applications. The developed lensless microscopy model enables the generation of datasets for training neural networks. Full article

A class of partial differential equations with random noise is employed to model the pipe acoustic system. A high-precision compact differential scheme is constructed for its solution. To ensure numerical stability, a buffer layer technique is applied to absorb outgoing waves. The propagation of acoustic waves under different modes is simulated. Furthermore, a specific numerical example is provided, and the results show good agreement with theoretical analysis. Full article

A monolayer metasurface-based Linear Polarizer and Quarter-Wave Plate (LP&QWP) is proposed for compact and precise polarization control in chip-scale atomic clocks (CSACs). Finite-difference time-domain simulations reveal that the designed metasurface efficiently converts linearly polarized light into right-handed circularly polarized light. Experimental characterization of devices fabricated on optical glass substrates confirms the polarization manipulation performance, achieving a polarization extinction ratio (PER) of 4.8 dB and a degree of polarization (DOP) of 74.2%, confirming its ability to effectively control the state of polarization. The short-term frequency stability of the developed CSAC prototype reaches 9.29 × 10⁻¹¹ at 1 s and 1.59 × 10⁻¹¹ at 10,000 s, demonstrating its potential for integration into miniature timing systems. The novelty of this work lies in the specific application to CSACs and the co-optimization with attenuation, as the metasurface simultaneously realizes polarization control and optical power balancing within a single functional layer. This study bridges metasurface photonics and atomic frequency standards, providing a functional route toward polarization control and frequency stability in miniaturized chip-scale atomic clocks. Full article

Soil compaction in grassland and agricultural soils reduces water infiltration, root growth and ecosystem services. Conventional deep tillage and coring can alleviate compaction but are energy intensive and strongly disturb the turf. This study proposes an earthworm-inspired subsurface robot as a low-disturbance loosening tool for compacted grassland soils. Design principles are abstracted from earthworm body segmentation, anchoring–propulsion peristaltic locomotion and corrugated body surface, and mapped onto a robotic body with anterior and posterior telescopic units, a flexible mid-body segment, a corrugated outer shell and a brace-wire steering mechanism. Kinematic simulations evaluate the peristaltic actuation mechanism and predict a forward displacement of approximately 15 mm/cycle. Using the finite element method and a Modified Cam–Clay soil model, different linkage layouts and outer-shell geometries are compared in terms of radial soil displacement and drag force in cohesive loam. The optimised corrugated outer shell combining circumferential and longitudinal waves lowers drag by up to 20.1% compared with a smooth cylinder. A 3D-printed prototype demonstrates peristaltic locomotion and steering in bench-top tests. The results indicate the potential of earthworm-inspired subsurface robots to provide low-disturbance loosening in conservation agriculture and grassland management, and highlight the need for field experiments to validate performance in real soils. Full article

A compact high-gain antipodal Vivaldi antenna with ultra-wideband (UWB) performance ranging from 1 GHz to 25 GHz is proposed and demonstrated. The antenna features two sets of tapered exponential slots along the flare edges to enhance low-frequency impedance matching and broaden the operating bandwidth. To address high-frequency gain degradation, a rhombus-shaped metamaterial array is embedded within the tapered slot region, effectively improving radiation directivity and suppressing gain roll-off without enlarging the antenna footprint. Full-wave simulations and experimental measurements confirm that the proposed antenna achieves a well-matched impedance bandwidth from 1 to 25 GHz, with a peak gain of 15.84 dBi. Notably, the gain remains consistently above 14 dBi in the high-frequency region, verifying the effectiveness of the embedded metamaterial structure. The proposed design successfully balances wideband operation, high gain, and compact form factor, offering a promising solution for space-constrained UWB applications in communication, sensing, and imaging systems. Full article

Planetary journal bearings are enablers for wind turbine gearbox torque density and reliability increase due to their compactness and potentially unlimited lifetime. They are designed to withstand the load conditions during wind turbine operation. Despite their general robustness, abnormal events such as particle contamination, strong overload or operation without sufficient oil supply may be harmful to the bearings. In these cases, damage can occur quickly and with little warning time. Such spontaneous failure leads to turbine downtime and cost-intensive repair work on the wind turbine drive train. Thus, reliable load and condition monitoring systems, which allow the detection of critical operating states before damage occurs, would be beneficial. For journal bearings in wind turbine gearboxes, no commercially available monitoring system exists to date. The existing studies on journal bearing condition monitoring are limited to experiments on component test rigs or small gearboxes, and their transferability to full-size systems has yet to be proven. This work presents the results of a system test with an 850 kW wind turbine gearbox equipped with planetary journal bearings and a novel condition monitoring system based on the measurement of surface acoustic waves. First, the journal bearing design, including the sensor setup, is explained. Second, the test campaign layout is presented. The gearbox is tested under load conditions specific to wind turbines, and the condition monitoring signals are examined in detail. An algorithm based on a machine learning model is presented for evaluating the monitoring signals and predicting the friction state of the bearings. Finally, the practical feasibility and quality of the monitoring approach for planetary journal bearings presented in this work is discussed. Full article

In this study, we present the design and analysis of a stacked inverter-based millimeter-wave (mmWave) power amplifier (PA) in 90 nm CMOS-targeting wideband Q-band operation. The PA employs two PMOS and two NMOS devices in a fully stacked inverter topology to distribute device stress, remove the need for an RF choke, and increase effective transconductance while preserving compact layout. A resistor ladder biases the stack near $V_{\mathrm{DD}}/4$ per device, and capacitive division steers intermediate-node swings to enable class-E-like voltage shaping at the output. Closed-form models are developed for gain, output power, drain efficiency/PAE, and linearity, alongside a small-signal stacked-ladder formulation that quantifies stress sharing and the impedance presented to the matching networks; L/T network synthesis relations are provided to co-optimize bandwidth and insertion loss. Post-layout simulation in 90 nm CMOS shows $|S_{21}|$ = 10 dB at 39.84 GHz with 3 dB bandwidth from 36.8 to 42.4 GHz, peak PAE of 18.38% near 41 GHz, and saturated output power $P_{\mathrm{sat}}=8.67$ dBm at $V_{\mathrm{DD}}=4$ V, with $S_{11}<-15$ dB and reverse isolation $\approx -16$ dB. The layout occupies $1.6\times 1.6$ mm² and draws 31.08 mW. Robustness is validated via a 200-run Monte Carlo showing tight clustering of $P_{\mathrm{sat}}$ and PAE, sensitivity sweeps identifying sizing/tolerance trade-offs ($\pm 10\%$ devices/passives), and EM co-simulation of on-chip passives indicating only minor loss/shift relative to schematic while preserving the target bandwidth and efficiency. The results demonstrate a balanced gain–efficiency–power trade-off with layout-aware resilience, positioning stacked-inverter CMOS PAs as a power- and area-efficient solution for mmWave front-ends. Full article

Rydberg atoms are neutral atoms excited to high principal quantum number states, which endows them with exaggerated properties such as large electric dipole moments, long lifetimes, and extreme sensitivity to external electromagnetic fields. These characteristics form the foundation of Rydberg atom-based sensors, an emerging class of quantum devices capable of optically detecting electric fields across frequencies from DC to the terahertz regime. Rydberg-based electrometry operates through both Autler–Townes (AT) splitting of resonant Rydberg transitions and Stark-shift measurements for high-frequency or far-detuned fields, enabling broadband field sensing from DC to the THz regime. Using ladder-type electromagnetically induced transparency (EIT) and AT splitting, these sensors enable non-invasive, SI-traceable measurements of field amplitude, frequency, phase, and polarization. Recent developments have demonstrated broadband electric field probes, voltage calibration standards, and compact RF receivers based on thermal vapor cells and integrated photonic architectures. Furthermore, innovations in multi-photon EIT, superheterodyne readout, and multi wave mixing have expanded the dynamic range and bandwidth of Rydberg-based electrometry. Despite challenges related to environmental perturbations, linewidth broadening, and laser stabilization, ongoing advances in atomic control, hybrid photonic integration, and EIT-based readout promise scalable, chip-compatible sensors. This review summarizes the physical principles, experimental progress, and emerging applications of Rydberg atom-based sensing, emphasizing their potential for next generation quantum metrology, wireless communication, and precision field mapping. Full article

This work investigates how the vehicle-to-tube suspension gap governs compressible flow physics and operating margins in Hyperloop-class transport at 10 kPa. To our knowledge, this is the first study to apply adjoint aerodynamic optimization to mitigate gap-induced choking and shock formation in a full pod–tube configuration. Using a steady, pressure-based Reynolds-averaged Navier-Stokes (RANS) framework with the GEnerlaized K-Omega (GEKO) turbulence model, a simulation for the cruise conditions was performed at M = 0.5–0.7 with a mesh-verified analysis (medium grid within 0.59% of fine) to quantify gap effects on forces and wave propagation. For small gaps, the baseline pod triggers oblique shocks and a near-Kantrowitz condition with elevated drag and lift. An adjoint shape update—primarily refining the aft geometry under a thrust-equilibrium constraint—achieves 27.5% drag reduction, delays the onset of choking by ~70%, and reduces the critical gap from d/D ≈ 0.025 to ≈0.008 at M = 0.7. The optimized configuration restores a largely subcritical passage, suppressing normal-shock formation and improving gap tolerance. Because propulsive power at fixed cruise scales with drag, these aerodynamic gains directly translate into operating-power reductions while enabling smaller gaps that can relax tube-diameter and suspension mass requirements. The results provide a gap-aware optimization pathway for Hyperloop pods and a compact design rule-of-thumb to avoid choking while minimizing power. Full article

To address the lack of compact and high-performance gas sensors in the literature, a miniaturized photoacoustic sensor has been developed using a resonant capacitive MEMS specifically designed for gas detection. Its performance is enhanced by coupling it to a T-shaped acoustic cavity, which confines and directs the acoustic waves toward the transducer. Electrical and photoacoustic characterizations were carried out to determine the nominal capacitance and resonance frequency of the device. The acoustic coupling resulted in a significant improvement in the transducer’s mechanical response, while the linearity of the sensor was confirmed over a broad concentration range. This improvement led to a reduction in the limit of detection (LOD) from 186 ppmv to 16 ppmv. In parallel, the Normalized Noise-Equivalent Absorption ($NNEA$) metric improved from $1.49\times {10}^{-7}\mathrm{W}·{\mathrm{cm}}^{-1}·{\mathrm{Hz}}^{-1/2}$ to $1.28\times {10}^{-8}\mathrm{W}·{\mathrm{cm}}^{-1}·{\mathrm{Hz}}^{-1/2}$, representing a 11-fold increase in sensitivity. Stability over time is confirmed through Allan–Werle deviation analysis, confirming the reliability of the signal over extended measurement periods. These results demonstrate that coupling a resonant MEMS transducer to a well-designed acoustic cavity is an efficient strategy to significantly improve the sensitivity of photoacoustic gas detection systems. Full article

Dark matter constitutes the predominant component of the universe, yet its fundamental nature remains elusive, motivating diverse physical and astrophysical investigations. Recently, gravitational waves have emerged as a new probe for detecting the distribution of dark matter around massive black holes by measuring the dynamical friction exerted on compact objects within their orbits. The dark matter density profile plays a critical role in such analyses. In this study, we compute the relativistic density and velocity distributions of dark matter surrounding Schwarzschild black holes and develop a corresponding Python package DAMPS (v1.0.0). We provide a detailed derivation of the theoretical framework, present numerical results for two types of initial dark matter profiles—Hernquist and single power-law—and demonstrate an application to gravitational-waveform calculations for extreme mass-ratio inspirals. We anticipate that this software tool will benefit the broader community and advance the understanding of black hole–dark matter systems important to future space-based gravitational-wave detectors. Full article

This study presents an in-depth investigation of an eccentric double-ring microwave resonator comprising two asymmetrically coupled conductive loops connected at a single point. The configuration was systematically analyzed using analytical modeling, full-wave electromagnetic simulations (Ansys HFSS), and experimental characterization. Analytical formulations based on the resonant condition of thin conductive rings provided theoretical estimates of the fundamental and higher-order eigenmodes, while simulations yielded accurate resonance frequencies, transmission responses, and electric field distributions. The transmission coefficient (S₂₁) exhibited two distinct resonance dips at 436 MHz and 708 MHz, confirming strong inter-ring coupling and hybrid mode formation. Electric field mapping revealed pronounced confinement within the resonator region (E > 170 V/m) and substantial attenuation of the transmitted field (E < 13 V/m), demonstrating efficient electromagnetic energy suppression. Experimental results showed excellent consistency with theoretical predictions. This paper aims to establish a compact, low-cost, and tunable resonant structure capable of frequency-selective attenuation and field confinement without using lossy materials. Unlike conventional symmetric resonators, the eccentric configuration enables enhanced coupling control and modal diversity, making it highly relevant for the design of next-generation electromagnetic shielding, filtering, and sensing systems. Full article

Quantum mechanics postulates wave–particle duality and assigns amplitudes of the form $e^{iS/\hslash }$, yet no existing formulation explains why physical observables depend only on the phase of the action. Here we show that if the quantum of action ${\hslash }_{\mathrm{geom}}$ is finite, the classical action manifold $\mathbb{R}$ becomes compact under the identification $S\equiv S+2\pi {\hslash }_{\mathrm{geom}}$, yielding a $U\left(1\right)$ action space on which only modular action is observable. Wave interference then follows as a geometric necessity: a finite action quantum forces physical amplitudes to live on a circle, while the classical limit arises when the modular spacing $2\pi {\hslash }_{\mathrm{geom}}$ becomes negligible compared with macroscopic actions. We formulate this as a compact-action theorem. Chronon Field Theory (ChFT) provides the physical origin of ${\hslash }_{\mathrm{geom}}$: its causal field ${\Phi }^{\mu }$ carries a quantized symplectic flux $\oint \omega ={\hslash }_{\mathrm{geom}}$, making Planck’s constant a geometric topological invariant rather than an imposed parameter. Within this medium, the Real–Now–Front (RNF) supplies a local reconstruction rule that reproduces the structure of the Feynman path integral, the Schrödinger evolution, the Born rule, and macroscopic definiteness as consequences of geometric compatibility rather than supplemental postulates. Phenomenologically, identifying the electron as the minimal chronon soliton—carrying the fundamental unit of symplectic flux—links its spin, charge, and stability to topological properties of the chronon field, yielding concrete experimental signatures. Thus the compact-action/RNF framework provides a unified geometric origin for quantum interference, measurement, and matter, together with falsifiable predictions of ChFT. Full article

To address the high computational cost and significant resource consumption of radar Doppler-based target recognition, which limits its application in real-time embedded systems, this paper proposes a lightweight CNN (Convolutional Neural Network) approach for radar target identification. The proposed approach builds a deep convolutional neural network using range-Doppler maps, and leverages data collected by frequency-modulated continuous wave (FMCW) radar from targets such as drones, vehicles, and pedestrians. This method enables efficient object detection and classification across a wide range of scenarios. To improve the performance of the proposed model, this study incorporates a coordinate attention mechanism within the convolutional neural network. This mechanism fine-tunes the network’s focus by dynamically adjusting the weights of different feature channels and spatial regions, allowing it to concentrate on the most informative areas. Experimental results show that the foundational architecture of the proposed deep learning model, RangDopplerNet Type-1, effectively captures micro-Doppler features from range-Doppler maps across diverse targets. This capability enables precise detection and classification, with the model achieving an impressive average recognition accuracy of 96.71%. The enhanced network architecture, RangeDopplerNet Type-2, reached an average accuracy of 98.08%, while retaining a compact footprint of only 403 KB. Compared with standard lightweight models such as MobileNetV2, the proposed architecture reduces model size by 97.04%. This demonstrates that, while improving accuracy, the proposed architecture also significantly reduces both computational and storage overhead.The deep learning model introduced in this study is specifically tailored for deployment on resource-constrained platforms, including mobile and embedded systems. It provides an efficient and practical approach for development of miniaturized low-power devices. Full article